forked from EngineX-Cambricon/enginex-mlu370-vllm
add qwen3
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Chranos
@@ -0,0 +1,208 @@
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import json
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from typing import Dict, Sequence, Union
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import partial_json_parser
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from partial_json_parser.core.options import Allow
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from vllm.entrypoints.openai.protocol import (ChatCompletionRequest,
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DeltaFunctionCall, DeltaMessage,
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DeltaToolCall,
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ExtractedToolCallInformation,
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FunctionCall, ToolCall)
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from vllm.entrypoints.openai.tool_parsers.abstract_tool_parser import (
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ToolParser, ToolParserManager)
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from vllm.entrypoints.openai.tool_parsers.utils import (
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extract_intermediate_diff)
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from vllm.logger import init_logger
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from vllm.transformers_utils.tokenizer import AnyTokenizer
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from vllm.utils import random_uuid
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logger = init_logger(__name__)
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@ToolParserManager.register_module(["internlm"])
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class Internlm2ToolParser(ToolParser):
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def __init__(self, tokenizer: AnyTokenizer):
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super().__init__(tokenizer)
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self.position = 0
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def adjust_request(
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self, request: ChatCompletionRequest) -> ChatCompletionRequest:
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if request.tools and request.tool_choice != 'none':
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# do not skip special tokens because internlm use the special
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# tokens to indicated the start and end of the tool calls
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# information.
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request.skip_special_tokens = False
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return request
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def get_argments(self, obj):
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if "parameters" in obj:
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return obj.get("parameters")
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elif "arguments" in obj:
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return obj.get("arguments")
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return None
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def extract_tool_calls_streaming(
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self,
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previous_text: str,
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current_text: str,
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delta_text: str,
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previous_token_ids: Sequence[int],
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current_token_ids: Sequence[int],
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delta_token_ids: Sequence[int],
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request: ChatCompletionRequest,
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) -> Union[DeltaMessage, None]:
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if '<|action_start|>' not in current_text:
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self.position = len(current_text)
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return DeltaMessage(content=delta_text)
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# if the tool call is sended, return a empty delta message
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# to make sure the finish_reason will be send correctly.
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if self.current_tool_id > 0:
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return DeltaMessage(content='')
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last_pos = self.position
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if '<|action_start|><|plugin|>' not in current_text[last_pos:]:
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return None
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new_delta = current_text[last_pos:]
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text, action = new_delta.split('<|action_start|><|plugin|>')
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if len(text) > 0:
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self.position = self.position + len(text)
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return DeltaMessage(content=text)
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action = action.strip()
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action = action.split('<|action_end|>'.strip())[0]
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# bit mask flags for partial JSON parsing. If the name hasn't been
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# sent yet, don't allow sending
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# an incomplete string since OpenAI only ever (as far as I have
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# seen) allows sending the entire tool/ function name at once.
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flags = Allow.ALL if self.current_tool_name_sent \
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else Allow.ALL & ~Allow.STR
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try:
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parsable_arr = action
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# tool calls are generated in an object in inernlm2
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# it's not support parallel tool calls
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try:
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tool_call_arr: Dict = partial_json_parser.loads(
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parsable_arr, flags)
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except partial_json_parser.core.exceptions.MalformedJSON:
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logger.debug('not enough tokens to parse into JSON yet')
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return None
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# if the current tool name hasn't been sent, send if available
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# - otherwise send nothing
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if not self.current_tool_name_sent:
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function_name = tool_call_arr.get("name")
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if function_name:
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self.current_tool_id = self.current_tool_id + 1
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delta = DeltaMessage(tool_calls=[
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DeltaToolCall(index=self.current_tool_id,
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type="function",
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id=f"chatcmpl-tool-{random_uuid()}",
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function=DeltaFunctionCall(
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name=function_name).model_dump(
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exclude_none=True))
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])
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self.current_tool_name_sent = True
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self.streamed_args_for_tool.append("")
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else:
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delta = None
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# now we know we're on the same tool call and we're streaming
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# arguments
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else:
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prev_arguments = self.get_argments(
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self.prev_tool_call_arr[self.current_tool_id])
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cur_arguments = self.get_argments(tool_call_arr)
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# not arguments generated
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if not cur_arguments and not prev_arguments:
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delta = None
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# will never happen
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elif not cur_arguments and prev_arguments:
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logger.error(
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"INVARIANT - impossible to have arguments reset "
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"mid-arguments")
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delta = None
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# first time to get parameters
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elif cur_arguments and not prev_arguments:
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cur_arguments_json = json.dumps(cur_arguments)
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arguments_delta = cur_arguments_json[:cur_arguments_json.
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index(delta_text) +
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len(delta_text)]
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delta = DeltaMessage(tool_calls=[
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DeltaToolCall(index=self.current_tool_id,
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function=DeltaFunctionCall(
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arguments=arguments_delta).
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model_dump(exclude_none=True))
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])
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self.streamed_args_for_tool[
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self.current_tool_id] += arguments_delta
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# both prev and cur parameters, send the increase parameters
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elif cur_arguments and prev_arguments:
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cur_args_json = json.dumps(cur_arguments)
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prev_args_json = json.dumps(prev_arguments)
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argument_diff = extract_intermediate_diff(
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cur_args_json, prev_args_json)
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delta = DeltaMessage(tool_calls=[
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DeltaToolCall(index=self.current_tool_id,
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function=DeltaFunctionCall(
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arguments=argument_diff).model_dump(
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exclude_none=True))
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])
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self.streamed_args_for_tool[
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self.current_tool_id] += argument_diff
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# check to see if the name is defined and has been sent. if so,
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# stream the name - otherwise keep waiting
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# finish by setting old and returning None as base case
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tool_call_arr["arguments"] = self.get_argments(tool_call_arr)
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self.prev_tool_call_arr = [tool_call_arr]
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return delta
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except Exception:
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logger.exception("Error trying to handle streaming tool call.")
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logger.debug(
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"Skipping chunk as a result of tool streaming extraction "
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"error")
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return None
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def extract_tool_calls(
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self,
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model_output: str,
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request: ChatCompletionRequest,
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) -> ExtractedToolCallInformation:
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text = model_output
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tools = request.tools
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if '<|action_start|><|plugin|>' in text:
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text, action = text.split('<|action_start|><|plugin|>')
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action = action.split('<|action_end|>'.strip())[0]
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action = action[action.find('{'):]
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action_dict = json.loads(action)
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name, parameters = action_dict['name'], json.dumps(
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action_dict.get('parameters', action_dict.get('arguments',
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{})))
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if not tools or name not in [t.function.name for t in tools]:
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ExtractedToolCallInformation(tools_called=False,
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tool_calls=[],
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content=text)
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tool_calls = [
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ToolCall(
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function=FunctionCall(name=name, arguments=parameters))
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]
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return ExtractedToolCallInformation(
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tools_called=True,
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tool_calls=tool_calls,
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content=text if len(text) > 0 else None)
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return ExtractedToolCallInformation(tools_called=False,
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tool_calls=[],
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content=text)
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